The sorting of male and female silkworm pupae is an essential process of silkworm breeding, with its accuracy directly affecting the quality of hybrid silkworm eggs and silk. Gonadal characteristics serve as a reliable basis for sex identification in silkworm pupae; however, the gonads only exist on the positive side of the tail. Due to the unique geometry of silkworm pupae, online sex recognition based on machine vision requires flipping and taking many photos of the same silkworm pupae. Thus, accurately selecting the frontal image from multiple images of the same silkworm pupae in different poses is a prerequisite for subsequent sex identification. To address this challenge, we proposed SPNet-GS (Silkworm Pupae Network for Gonad Selection), a lightweight model for online selection of frontal silkworm pupae images. The model first employed a large kernel convolution to enhance the receptive field and capture the relevant information between adjacent pixels. Then the correlation between long-distance pixels under multi-scale information can be obtained by dilated convolutions. Finally, the correlation information between near and far pixels was fused to enhance feature extraction. Experimental results demonstrated that our method outperforms other models with an average accuracy of 98.41% and an average F1 score of 99.02%. The average inference time of each image was 0.03 s, which can fully meet the requirements of online selection of male and female silkworm pupae. Moreover, the gender identification accuracy rates using the selected frontal image and gonad region image reached 84.68% and 94.58%, respectively. These results were 10% and 19.90% higher than using multi-pose images for sex identification, demonstrating the effectiveness of the frontal image selection strategy. The findings of this investigation may provide a valuable reference for the machine vision-based intelligent online sorting of silkworm pupae by gender.

Dangshan pear is one of the most popular fruits in China. Among them, the surface defects can often be required to monitor the pear in real time. However, the practical requirements are limited to the computing resources of the devices under real-time constraints in industrial environments. In this study, a lightweight and efficient detection model (named AIC-YOLOv11n) was developed using the YOLOv11n architecture. Specifically, an Adown down-sampling module was introduced into the backbone. Both the floating-point and parameters were reduced to enhance the feature extraction. Additionally, the original C2PSA module was replaced with the C2PSA-iRMB one. An inverted residual mobile block (iRMB) was integrated with the attention mechanisms in order to efficiently capture the long-range dependencies with less computational overhead. Moreover, a cross-scale feature fusion module (CFFM) was employed in the neck structure of the network. Some features at different scales were effectively merged to improve the detection accuracy of the small-scale defects. A dataset with 5,000 labeled images was constructed to validate the performance of the improved model. The images were also collected using the conveyor-belt multi-surface imaging system, that equipped with synchronized upper and lower illumination boxes and industrial-grade cameras. The dataset included five categories: Calyx, stem-end cap, scratches, rust spots, and mold spots. Data augmentation was also carried out, including rotation, flipping, and brightness adjustments. The dataset was then partitioned into the training, validation, and test datasets at an 8:1:1 ratio. Experimental results showed that the improved AIC-YOLOv11n model achieved better performance in detection, compared with the baseline YOLOv11n. Specifically, there was a precision of 92.5%, a recall rate of 87.5%, an mAP_0.5, of 92.7%, and an mAP_0.5-0.95 of 70.5%, which were improved by 0.3, 5.5, 5.1, and 2.4 percentage points, respectively. Additionally, the computational costs were reduced significantly to require only 4.3 G, 1.46 million parameters, and a model size of 3.11 MB, which were reduced by 31.7%, 43.4%, and 40.5%, respectively. Furthermore, the peak GPU memory usage remained below 4.83 GB, and the inference speed reached 120.1 frames per second (FPS), thus fully meeting the real-time requirement of the defect inspection. Ablation studies demonstrated that there were the great contributions of the three modules. Among them, the Adown achieved the greatest improvement in the recall, while the CFFM significantly enhanced the detection accuracy of the small objects, and C2PSA-iRMB effectively increased the precision. Grad-CAM visualization further confirmed that the improved model was focused accurately on the defect regions, while suppressing the interference from normal anatomical structures. Online TensorRT deployment was then utilized to validate the improved model in an industrial scenario. Once converted to a TensorRT FP16 inference engine, there was a single-image inference latency of just 1.4 ms without compromising accuracy, indicating its suitability for real-world applications. In conclusion, the AIC-YOLOv11n was provided to balance the accurate, efficient, and lightweight surface defect detection on Dangshan pears. Model pruning, knowledge distillation, and transfer learning can be expected for the more fruit types in agricultural industries.

Smart agriculture is a promising potential direction in modern agriculture worldwide. However, China’s agriculture is still limited by multiple constraints at present, such as resources, markets, and the environment. Therefore, smart agriculture can be expected to take as a breakthrough point, in order to promote the agricultural transformation and modernization. In this study, a systematic analysis was made on the current status of the research and application of the key technologies and equipment in smart agriculture. The main challenges were clarified to prospect the development trend and promotion path of China’s smart agriculture in the future. Specifically, 1) Smart agriculture serves as the modern advanced form to take the information and knowledge as the core elements. Smart agriculture aimed to realize the digital management, intelligent decision-making, automatic operation, precise input, and networked services, after the deep integration of advanced productive forces, such as modern information, industrial equipment, and agricultural biotechnology. 2) China’s smart agriculture relied mainly on three common supports: sensor and perception systems, decision-making models and algorithms, as well as intelligent platforms. Remarkable progress was made in the R＆D of sensor technologies, and the agricultural satellite remote sensing, as well as the agricultural unmanned aerial vehicle remote sensing. Agricultural basic algorithms were continuously optimized to couple the animal and plant models, gradually shifting towards a multi-factor system. The R＆D of large agricultural artificial intelligence models was to facilitate the intelligent platform architecture. Data storage and management technologies were developed to integrate the application using knowledge graphs, indicating the hot spot. 3) The application scenarios of smart agriculture in China were constantly expanding under advanced technology. a) The frontier technologies, such as artificial intelligence and gene editing, were promoted to transform the breeding paradigm towards intelligence. b) The technical system of smart farms was gradually improved to initially achieve ‘machine replacing people’. c) Smart facility agriculture has advanced significantly to produce the core equipment in the construction of a high-end intelligent greenhouse. d) The livestock and poultry breeding have achieved significant advances in smart technology. The smart farms for dairy, pork, and poultry were at the top worldwide. e) Smart fishery was limited to the sensors, growth models, and special robots. A number of smart fish farms were constructed to accelerate industrialization. f) Smart agricultural product supply chains were still in the initial stage. The partial breakthroughs and application demonstrations were also observed in post-harvest processing, quality traceability, and smart storage and transportation. 4) There were still many challenges in smart agriculture, in terms of the key technology R＆D, technology transfer, and industrialization, and basic support systems. a) The basic model algorithms were insufficient to substitute for the high-end sensors and robots. b) The scientific and technological achievements were necessary to translate into practical applications. The chain ecosystem of the digital industry was also required to evaluate the integration and sustainability of application scenarios. c) The decision-making on the data resource was also required to strengthen the professional talents and farmers’ digital literacy. 5) Looking to the future, five strategic directions were recommended in China’s smart agriculture: the low-cost and high-precision agricultural information perception, agricultural artificial intelligence, agricultural low-altitude economy, small-scale smart agriculture, and ‘zero-carbon’ smart agriculture. The institutional framework and ‘digital foundation’ of smart agriculture should be consolidated to promote smart agriculture in China. Universities and research institutions should promote the R＆D of key technologies for smart agriculture and the cultivation of high-end talents. Technology-based enterprises should construct a market-oriented and sustainable smart agriculture industrial cluster. Agricultural business entities and farmers also need to promote the practical application of ‘scenario + chain’ in smart agriculture.

Swarm intelligence in agricultural robotics has emerged as a strategic frontier that can simultaneously boost productivity and sustainability in modern agriculture. By enabling safe, reliable, autonomous, and efficient operations across open, dynamic, and partially structured farm environments, swarm intelligence provides a unifying paradigm for transitioning from single-robot autonomy to coordinated multi-robot systems. This paper presents a systematic review of the fundamental connotation, elements, key technologies, and future directions of agricultural robot swarm intelligence. We first clarify the concept and origins of swarm intelligence and summarize its defining properties—distributed control, self-organization, and dynamic adaptability—then propose a domain-specific conceptual framework for agricultural robot swarms centered on “Individual autonomy–Information sharing–Swarm collaboration.” We then delve into the four critical technological pillars underpinning agricultural robot swarm intelligent (ARSI): collaborative perception, collaborative planning, collaborative control, and ground-air cross-domain collaboration. For collaborative perception, we discuss multi-robot localization and mapping in non-structured fields, emphasizing robustness under GNSS multipath, vegetation occlusion, and seasonal appearance changes. Representative SLAM and semantic mapping strategies are compared, including centralized and fully distributed map fusion, bandwidth-aware loop closure, and spatiotemporal synchronization across heterogeneous sensors. We highlight advances that fuse LiDAR-vision-inertial data, improve robustness in highly dynamic scenes, and maintain long-term map consistency for seasonal operations. In collaborative planning, we review task allocation and motion planning under large task scales, heterogeneous robot capabilities, and tight operation windows. Four families of task assignment methods are contrasted—exact optimization, market/auction mechanisms, bio-inspired metaheuristics, and learning-driven approaches—together with their trade-offs in optimal, scalability, and responsiveness. For coverage and path planning, we summarize graph-search, sampling-based, potential-field, and intelligent optimization methods, and discuss trajectory tracking for synchronized multi-robot execution using techniques such as artificial potential fields, spline smoothing, and model predictive control. For collaborative control, we briefly review formation and consensus methods (leader-follower, behavior-based control, event-triggered schemes, and predictive control) that ensure safety, precision, and resource efficiency in long-duration field operations. Special attention is given to region-reaching consensus for multi-task area operations, event-triggered formation control that reduces computation/communication loads while preserving tracking accuracy, and robust schemes that accommodate model uncertainties. We then analyze ground–air cross-domain collaboration, wherein UAVs provide wide-area monitoring, semantic mapping, and communication relays, while UGVs execute high-precision, energy-efficient interventions. We discuss registration and fusion between aerial and ground maps, closed-loop pipelines from perception to targeted actuation (e.g., site-specific weeding/spraying), and the scalability challenges of communication, safety, and real-time scheduling in large deployments. In addition, we present compelling case studies from global initiatives and commercial deployments, such as the RHEA project’s weed control system, SwarmFarm’s cloud-optimized fleet, and Carbon Robotics’ laser-weeding technology, demonstrating tangible benefits in reducing chemical inputs and labor costs. Finally, we outline five key future research directions: 1) the development of domain-specific agricultural foundation models to drive knowledge-informed decision-making; 2) the creation of comprehensive “Farm-Digital Twin” platforms for closed-loop simulation and re-planning; 3) the adoption of hierarchical edge-cloud architectures to manage computational and communication bottlenecks; 4) the advancement of dynamic, game-theoretic planning frameworks for highly uncertain environments; and 5) the standardization and large-scale validation of ground-air collaborative systems. This synthesis aims to provide researchers and industry stakeholders with a clear roadmap for advancing the field and accelerating the deployment of intelligent, scalable, and sustainable robotic solutions for the future of agriculture.

Monitoring the growth dynamics in field-grown cabbage is critically important for ensuring stable vegetable production and advancing precision agricultural management. However, conventional two-dimensional (2D) image-based monitoring approaches are limited to planar projection information and lack representations of spatial structural characteristics, rendering them inadequate for supporting high-precision, full-cycle phenotypic monitoring of cabbage under open-field conditions. In this study, a high-precision three-dimensional (3D) point cloud dataset covering the period from the seedling stage to maturity was constructed using depth cameras in conjunction with multi-view spatial registration techniques. Building on this dataset, an adaptive point cloud segmentation network designed for the whole-cycle growth monitoring was proposed, incorporating a Head Refinement Module (HRM), a Leaf Instance Segmentation Module (LISM), and Cross Module Interaction (CMI) to address leaf adhesion and head boundary delineation. Experimental results demonstrated that the proposed method consistently outperformed state-of-the-art models in both semantic and instance segmentation tasks. For semantic segmentation, the mean Intersection over Union (mIoU) reached 0.767, with a point classification accuracy of 94.8%. The model comprises 54.25 million parameters and achieves an average response time of 0.76 s. For instance segmentation, the Average Precision (AP) improved by 2.3% for cabbage heads and 3.8% for leaves, while the Average Recall (AR) increased by 6.9%. Growth parameters, including plant height and canopy spread, extracted from the segmentation results showed strong agreement with ground-truth measurements, with correlation of coefficients (R²) exceeding 0.9 for plant height, canopy length, and canopy width. Leveraging these multidimensional phenotypic descriptors, the temporal dynamics of cabbage growth throughout the entire growth cycle were systematically characterized. Overall, this study enables dynamic monitoring of cabbage phenotypes across the full growth cycle, providing a novel technical pathway for extending 3D phenotyping from controlled environments to open-field applications and offering important support for precise crop monitoring and the development of digital twin agriculture.

Rapid and accurate identification of DUS (Distinctness, Uniformity, and Stability) test traits in lettuce leaves is essential for advancing multi-omics-driven intelligent breeding. It also plays a critical role in germplasm protection and enhancing agricultural competitiveness. However, the phenotypic traits of lettuce leaves are highly diverse and complex due to both genotypic variation and environmental influences, posing significant challenges for precise DUS trait quantification. To address these challenges, we propose a high-precision phenotypic trait extraction pipeline and introduce an interpretable phenotypic fingerprinting framework for lettuce subgroup identification. First, a lightweight semantic segmentation network guided by group attention is developed to extract leaf components. Then, shape, color, and texture traits are comprehensively quantified. Following UPOV (International Union for the Protection of New Varieties of Plants) guidelines, we establish quantitative methods for seven DUS test traits: leaf shape, leaf tip shape, leaf margin shape, leaf vein shape, color hue, brightness, and anthocyanin coloration. Finally, PCA (Principal component analysis) was used to select 13 key traits, capturing over 95.82% of the total variance, for constructing “phenotypic ID” of lettuce varieties. Experiments conducted on 709 lettuce leaf image datasets showed that the accuracy of subgroup identification based on phenotypic fingerprints reached 98.59%. This study offers a scalable approach for automated DUS test trait evaluation and intelligent crop variety identification, providing a novel paradigm with strong potential for application in precision breeding and germplasm resource management.

As the crop with the longest planting history, widest planting area, and highest yield in hilly and mountainous areas, the improvement of the comprehensive mechanization level of rice cultivation and harvesting is of great significance for promoting the comprehensive and high-quality development of agricultural mechanization throughout the entire hilly and mountainous area. This study takes the key technology of rice mechanization in hilly and mountainous areas as the incision, and based on the characteristics of hilly and mountainous areas in China and the current development status of rice mechanization in hilly and mountainous areas, conducts a technological frontier and trend analysis around the production mechanization of rice tillage, planting, harvesting and other main links, analyzes the gap in research and development of rice mechanization technology and equipment at home and abroad, explores the challenges faced by the development of rice mechanization in hilly and mountainous areas in China under different links, and looks forward to the future development trend of rice mechanization in hilly and mountainous areas, in order to provide new ideas and directions for promoting the overall mechanization level of rice production, filling the gaps in the development of agricultural mechanization in hilly and mountainous areas, and building up China's strength in agriculture. This study proposes that, the comprehensive mechanization rate of rice cultivation and harvesting in hilly and mountainous areas of China in 2022 is 80.8%, and the development is unbalanced and insufficient among different links and regions; Compared with advanced countries, the research and application of rice cultivation and harvesting machinery in China started relatively late, with a weak foundation and low comprehensive performance of products. Especially, there is a relative shortage of lightweight, efficient, and highly stable rice cultivation and harvesting machinery for hilly and mountainous areas. Key technologies such as engine high-power low emission design and manufacturing technology, tool wear resistance and consumption reduction optimization design technology, and frame lightweight design technology still need to be further overcome; Lightweight, intelligent, and green technologies are the future directions and trends for the development of rice production equipment in hilly and mountainous areas of China.

To address the dynamic and uncertain challenges posed by climate variability, soil heterogeneity, water distribution, and other key factors in crop field production, intelligent decision support systems (IDSS) that integrate domain knowledge and multi-source data for irrigation, fertilization, pest and disease control, and field dynamic management, are of great importance in meeting modern agriculture’s demands for high precision, efficiency, and sustainability. By encompassing the development stages, typical practices, and technological pathways in China and developed countries, this paper summarizes the representative progress in Internet of Things (IoT), multimodal fusion, knowledge representation, reinforcement learning, reasoning, and practical applications in IDSS. Prominent research challenges include the lack of real-time or near-real-time sensor data, static domain knowledge, poor multimodal decision-making capability, weak cross-field generalization, and various implementation barriers, such as a vague definition of data governance, high costs of service infrastructure, and low user acceptance intention. To overcome these challenges, future research should prioritize the development of scalable, dynamic, robust, interpretable, and trustworthy multimodal IDSS, promote the formulation of standards, and establish an open platform for seamless model deployment, thereby facilitating the transformation from experience-driven to intelligence-driven agricultural production paradigms.

Citrus surface defects play a pivotal role in the fruit inspection and grading during agricultural production. Surface imperfections are also much easier to spot than inside ones, leading to accelerate the deterioration. However, conventional detection of citrus surface defects cannot fully meet the overall quality assessment in the large-scale production in recent years, due to the low efficiency and accuracy. In this study, an accurate, rapid and real-time detection was proposed to consider the diverse and complex nature of surface imperfections observed in citrus fruits. This speed and precision of detection were also enhanced for the quality of surface defects. Firstly, the images of citrus fruits were captured by industrial cameras. The generated images were enhanced to make the region of interest more outstanding. Then, the YOLOv7-CACT model was improved for the defect region in the enhanced citrus image. The coordinate attention (CA) module was introduced in the backbone network, in order to increase the attention to the defective part. The contextual transformer (CT) module was introduced in the head of the network to fuse the static and dynamic contextual representation features, thus enhancing the feature expression of the defective part. The superior performance was achieved in the modified YOLOv7-CACT model, compared with the baseline version. Especially, the detection accuracy was improved by 4.1% in the mean average precision (mAP). Consequently, the modified model was fully met the accuracy requirements for the identification of citrus surface defects in an offline setting. TensorRT was also employed around YOLOv7-CACT for the deployment of improved model, in order to real-time detect in practical scenarios. The results show that the improved YOLOv7-CACT-RT model was performed the best to detect the surface defects on the surface of citrus fruits in the grading and sorting production line with a transition rate of 10 fruits per second. The deployed YOLOv7-CACT-RT model was loaded into the grading software programmed by C++ language, in order to validate the performance. An online detection was conducted on 198 mixed normal and defective citrus fruits on the sorting line, achieving a detection accuracy of 94.4%. The improved model can be directly applied to grade and sort fruit in the production line, according to the external qualities. Meanwhile, this model can also be extended to the real-time surface defects detection of other fruits without specialized knowledge. Our future research will focus on the registration and fusion of RGB and NIR image, in order to improve the detection accuracy of fruit defects.